Existing methods [1]} have attempted to solve this task by learning cross-modal audio-visual features wherein representations for each modality are expected to benefit from the contexts surrounding the other modality.
While such cross-modal representations are indeed powerful, they may not always be beneficial across modalities and might come at the cost of event detection performance for a specific modality.
For the Look, Listen Parse (LLP) dataset [1]}, we believe the audio modality may not always benefit from the proposed cross-modal architecture for the following reasons.
Firstly, there exists a dataset skew in favour of the audio modality that renders video sequences inconsequential for detecting audio streams. E.g., there are approximately 600 videos with speech as an audio event, while only 120 videos list it as a video event. In Figure REF  we present the distribution of audio, visual and audio-visual events across all event categories.
As another example, visual features may be useless if the dog bark audio event occurs in the background of the scene.
Secondly, it may be argued that input features for audio (VGGish) [3]} are already well suited for audio parsing and the inclusion of cross-modal information from visual stream introduces noise into the resulting features.
Finally, the existing state-of-the-art architecture for AVVP [1]} attempts to project audio and visual features to a shared event label space using a common fully connected layer.
Such a common projection layer might hurt certain audio events that do not benefit from visual features. For example, doorbell ringing might often be an audio event in the background without any supporting visual cues.
